Characterization of micropatterned nanofibrous scaffolds for neural network activity readout for high-throughput screening

2010 ◽  
pp. n/a-n/a ◽  
Author(s):  
Lina Wang ◽  
William S. Kisaalita
Keyword(s):  
Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Network Activity ◽  
Nanofibrous Scaffolds

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

High-Throughput Screening and Characterization of a High-Density Soybean Mutant Library Elucidate the Biosynthesis Pathway of Triterpenoid Saponins

2019 ◽  
Vol 60 (5) ◽  
pp. 1082-1097 ◽  
Author(s):  
Panneerselvam Krishnamurthy ◽  
Yukiko Fujisawa ◽  
Yuya Takahashi ◽  
Hanako Abe ◽  
Kentaro Yamane ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
High Density ◽  
Biosynthesis Pathway ◽  
Mutant Library ◽  
Triterpenoid Saponins

scholarly journals A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Refractive Index ◽  
High Throughput ◽  
Neural Network Model ◽  
High Throughput Screening ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
High Refractive Index ◽  
Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

scholarly journals Discovery of Selective Cannabinoid CB2 Receptor Agonists by High-Throughput Screening

2017 ◽  
Vol 23 (4) ◽  
pp. 375-383 ◽  
Author(s):  
Lisa M. Ogawa ◽  
Neil T. Burford ◽  
Yu-Hsien Liao ◽  
Caitlin E. Scott ◽  
Ashley M. Hine ◽  
...  
Keyword(s):  
Side Effects ◽  
High Throughput ◽  
High Throughput Screening ◽  
Immune Modulation ◽  
Cannabinoid Receptors ◽  
Endocannabinoid System ◽  
Allosteric Modulators ◽  
Peripheral Tissues ◽  
Selective Agonists

The endocannabinoid system (ECS) plays a diverse role in human physiology ranging from the regulation of mood and appetite to immune modulation and the response to pain. Drug development that targets the cannabinoid receptors (CB1 and CB2) has been explored; however, success in the clinic has been limited by the psychoactive side effects associated with modulation of the neuronally expressed CB1 that are enriched in the CNS. CB2, however, are expressed in peripheral tissues, primarily in immune cells, and thus development of CB2-selective drugs holds the potential to modulate pain among other indications without eliciting anxiety and other undesirable side effects associated with CB1 activation. As part of a collaborative effort among industry and academic laboratories, we performed a high-throughput screen designed to discover selective agonists or positive allosteric modulators (PAMs) of CB2. Although no CB2 PAMs were identified, 167 CB2 agonists were discovered here, and further characterization of four select compounds revealed two with high selectivity for CB2 versus CB1. These results broaden drug discovery efforts aimed at the ECS and may lead to the development of novel therapies for immune modulation and pain management with improved side effect profiles.

scholarly journals Staphylococcus aureus DNA ligase: characterization of its kinetics of catalysis and development of a high-throughput screening compatible chemiluminescent hybridization protection assay

2004 ◽  
Vol 383 (3) ◽  
pp. 551-559 ◽  
Author(s):  
Sheraz GUL ◽  
Richard BROWN ◽  
Earl MAY ◽  
Marie MAZZULLA ◽  
Martin G. SMYTH ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
High Throughput ◽  
High Throughput Screening ◽  
Dna Ligase ◽  
Protection Assay ◽  
Dna Ligases ◽  
Enzyme Substrate ◽  
Ic50 Values ◽  
Acridinium Ester

DNA ligases are key enzymes involved in the repair and replication of DNA. Prokaryotic DNA ligases uniquely use NAD+ as the adenylate donor during catalysis, whereas eukaryotic enzymes use ATP. This difference in substrate specificity makes the bacterial enzymes potential targets for therapeutic intervention. We have developed a homogeneous chemiluminescence-based hybridization protection assay for Staphylococcus aureus DNA ligase that uses novel acridinium ester technology and demonstrate that it is an alternative to the commonly used radiometric assays for ligases. The assay has been used to determine a number of kinetic constants for S. aureus DNA ligase catalysis. These included the Km values for NAD+ (2.75±0.1 μM) and the acridinium-ester-labelled DNA substrate (2.5±0.2 nM). A study of the pH-dependencies of kcat, Km and kcat/Km has revealed values of kinetically influential ionizations within the enzyme–substrate complexes (kcat) and free enzyme (kcat/Km). In each case, the curves were shown to be composed of one kinetically influential ionization, for kcat, pKa=6.6±0.1 and kcat/Km, pKa=7.1±0.1. Inhibition characteristics of the enzyme against two Escherichia coli DNA ligase inhibitors have also been determined with IC50 values for these being 3.30±0.86 μM for doxorubicin and 1.40±0.07 μM for chloroquine diphosphate. The assay has also been successfully miniaturized to a sufficiently low volume to allow it to be utilized in a high-throughput screen (384-well format; 20 μl reaction volume), enabling the assay to be used in screening campaigns against libraries of compounds to discover leads for further drug development.

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